'''simple docstring'''
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ImageGPTImageProcessor







class lowerCamelCase				(						unittest.TestCase       ):




  '''simple docstring'''



  def __init__(   self      :							Dict	,				UpperCAmelCase__      :							Union[str, Any]	,				UpperCAmelCase__      :							Optional[Any]=7	,				UpperCAmelCase__      :							Tuple=3	,				UpperCAmelCase__      :							List[Any]=18	,				UpperCAmelCase__      :							Tuple=30	,				UpperCAmelCase__      :							List[str]=400	,				UpperCAmelCase__      :							Any=True	,				UpperCAmelCase__      :							Optional[int]=None	,				UpperCAmelCase__      :							Tuple=True	,				)					->Optional[int]:
        UpperCAmelCase_      =	size if size is not None else {'''height''': 18, '''width''': 18}
        UpperCAmelCase_      =	parent
        UpperCAmelCase_      =	batch_size
        UpperCAmelCase_      =	num_channels
        UpperCAmelCase_      =	image_size
        UpperCAmelCase_      =	min_resolution
        UpperCAmelCase_      =	max_resolution
        UpperCAmelCase_      =	do_resize
        UpperCAmelCase_      =	size
        UpperCAmelCase_      =	do_normalize






  def 			lowerCAmelCase__			(   self      :							Dict	)					->Optional[Any]:
        return {
            # here we create 2 clusters for the sake of simplicity
            "clusters": np.asarray(
                [
                    [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                    [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
                ]	),
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
        }







@require_torch
@require_vision
class lowerCamelCase				(						lowerCamelCase  ,		unittest.TestCase       ):




  '''simple docstring'''



  lowerCAmelCase__      				=						ImageGPTImageProcessor if is_vision_available() else None






  def 			lowerCAmelCase__			(   self      :							Optional[int]	)					->Dict:
        UpperCAmelCase_      =	ImageGPTImageProcessingTester(self	)






  @property
  def 			lowerCAmelCase__			(   self      :							List[str]	)					->Union[str, Any]:
        return self.image_processor_tester.prepare_image_processor_dict()






  def 			lowerCAmelCase__			(   self      :							List[Any]	)					->Optional[int]:
        UpperCAmelCase_      =	self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(UpperCAmelCase__	,				'''clusters'''	)	)
        self.assertTrue(hasattr(UpperCAmelCase__	,				'''do_resize'''	)	)
        self.assertTrue(hasattr(UpperCAmelCase__	,				'''size'''	)	)
        self.assertTrue(hasattr(UpperCAmelCase__	,				'''do_normalize'''	)	)






  def 			lowerCAmelCase__			(   self      :							Optional[Any]	)					->Union[str, Any]:
        UpperCAmelCase_      =	self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size	,				{'''height''': 18, '''width''': 18}	)

        UpperCAmelCase_      =	self.image_processing_class.from_dict(self.image_processor_dict	,				size=42	)
        self.assertEqual(image_processor.size	,				{'''height''': 42, '''width''': 42}	)






  def 			lowerCAmelCase__			(   self      :							str	)					->Any:
        UpperCAmelCase_      =	self.image_processing_class(**self.image_processor_dict	)
        UpperCAmelCase_      =	json.loads(image_processor.to_json_string()	)
        for key, value in self.image_processor_dict.items():
              if key == "clusters":
                    self.assertTrue(np.array_equal(UpperCAmelCase__	,				obj[key]	)	)
              else:
                    self.assertEqual(obj[key]	,				UpperCAmelCase__	)






  def 			lowerCAmelCase__			(   self      :							Any	)					->Tuple:
        UpperCAmelCase_      =	self.image_processing_class(**self.image_processor_dict	)

        with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase_      =	os.path.join(UpperCAmelCase__	,				'''image_processor.json'''	)
              image_processor_first.to_json_file(UpperCAmelCase__	)
              UpperCAmelCase_      =	self.image_processing_class.from_json_file(UpperCAmelCase__	).to_dict()

        UpperCAmelCase_      =	image_processor_first.to_dict()
        for key, value in image_processor_first.items():
              if key == "clusters":
                    self.assertTrue(np.array_equal(UpperCAmelCase__	,				image_processor_second[key]	)	)
              else:
                    self.assertEqual(image_processor_first[key]	,				UpperCAmelCase__	)






  def 			lowerCAmelCase__			(   self      :							str	)					->Tuple:
        UpperCAmelCase_      =	self.image_processing_class(**self.image_processor_dict	)

        with tempfile.TemporaryDirectory() as tmpdirname:
              image_processor_first.save_pretrained(UpperCAmelCase__	)
              UpperCAmelCase_      =	self.image_processing_class.from_pretrained(UpperCAmelCase__	).to_dict()

        UpperCAmelCase_      =	image_processor_first.to_dict()
        for key, value in image_processor_first.items():
              if key == "clusters":
                    self.assertTrue(np.array_equal(UpperCAmelCase__	,				image_processor_second[key]	)	)
              else:
                    self.assertEqual(image_processor_first[key]	,				UpperCAmelCase__	)






  @unittest.skip('''ImageGPT requires clusters at initialization'''	)
  def 			lowerCAmelCase__			(   self      :							List[Any]	)					->Any:
        pass






def 			__lowerCamelCase							(   ):


      '''simple docstring'''


      UpperCAmelCase_      =	load_dataset('''hf-internal-testing/fixtures_image_utils'''				,     split='''test'''       )

      UpperCAmelCase_      =	Image.open(dataset[4]['''file''']       )
      UpperCAmelCase_      =	Image.open(dataset[5]['''file''']       )

      UpperCAmelCase_      =	[imagea, imagea]

      return images







@require_vision
@require_torch
class lowerCamelCase				(						unittest.TestCase       ):




  '''simple docstring'''



  @slow
  def 			lowerCAmelCase__			(   self      :							List[str]	)					->Tuple:
        UpperCAmelCase_      =	ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''	)

        UpperCAmelCase_      =	prepare_images()

        # test non-batched
        UpperCAmelCase_      =	image_processing(images[0]	,				return_tensors='''pt'''	)

        self.assertIsInstance(encoding.input_ids	,				torch.LongTensor	)
        self.assertEqual(encoding.input_ids.shape	,				(1, 1024)	)

        UpperCAmelCase_      =	[306, 191, 191]
        self.assertEqual(encoding.input_ids[0, :3].tolist()	,				UpperCAmelCase__	)

        # test batched
        UpperCAmelCase_      =	image_processing(UpperCAmelCase__	,				return_tensors='''pt'''	)

        self.assertIsInstance(encoding.input_ids	,				torch.LongTensor	)
        self.assertEqual(encoding.input_ids.shape	,				(2, 1024)	)

        UpperCAmelCase_      =	[303, 13, 13]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()	,				UpperCAmelCase__	)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_big_bird import BigBirdTokenizer
else:
						__UpperCamelCase     		=							None

__UpperCamelCase     		=							logging.get_logger(__name__)
__UpperCamelCase     		=							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase     		=							{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCamelCase     		=							{
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}


__UpperCamelCase     		=							'▁'







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		VOCAB_FILES_NAMES
			_UpperCamelCase		:       int 			=		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:       List[Any] 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:       Optional[Any] 			=		BigBirdTokenizer
			_UpperCamelCase		:       Dict 			=		['input_ids', 'attention_mask']
			_UpperCamelCase		:       List[int] 			=		[]




			def __init__(				self   ,    snake_case=None   ,    snake_case=None   ,    snake_case="<unk>"   ,    snake_case="<s>"   ,    snake_case="</s>"   ,    snake_case="<pad>"   ,    snake_case="[SEP]"   ,    snake_case="[MASK]"   ,    snake_case="[CLS]"   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else bos_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else eos_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else unk_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else pad_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else cls_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else mask_token

						super().__init__(
						    snake_case   ,    tokenizer_file=snake_case   ,    bos_token=snake_case   ,    eos_token=snake_case   ,    unk_token=snake_case   ,    sep_token=snake_case   ,    pad_token=snake_case   ,    cls_token=snake_case   ,    mask_token=snake_case   ,    **snake_case   ,    )

						UpperCamelCase__       =							vocab_file
						UpperCamelCase__       =							False if not self.vocab_file else True




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]
						if token_ids_a is None:
									return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = False  ):

						'''simple docstring'''


						if already_has_special_tokens:
									if token_ids_a is not None:
												raise ValueError(
												    "You should not supply a second sequence if the provided sequence of "
												    "ids is already formatted with special tokens for the model."  )
									return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is None:
									return [1] + ([0] * len(snake_case  )) + [1]
						return [1] + ([0] * len(snake_case  )) + [1] + ([0] * len(snake_case  )) + [1]




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep  ) * [0]
						return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."  )

						if not os.path.isdir(snake_case  ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
									return
						UpperCamelCase__       =							os.path.join(
						    snake_case   ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

						if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case  ):
									copyfile(self.vocab_file   ,    snake_case  )

						return (out_vocab_file,)


